Attività del Dipartimento

Colloquium di Matematica

Advanced techniques for analyzing and understanding Big Data models are a crucial ingredient for the success of any supercomputing center and data intensive scientific investigation. Such techniques involve a number of major challenges such as developing scalable algorithms that run efficiently on the simulation data generated on the largest supercomputers in the world or incorporating robust methods are provably correct and complete in their extraction of features from the data.
In this talk, I will present the application of a discrete topological framework for the representation and analysis of large scale scientific data. Due to the combinatorial nature of this framework, we can implement the core constructs of Morse theory without the approximations and instabilities of classical numerical techniques. The inherent robustness of the combinatorial algorithms allows us to address the high complexity of the feature extraction problem for high resolution scientific data.
Our approach has enabled the successful quantitative analysis for several massively parallel simulations including the study turbulent hydrodynamic instabilities, porous material under stress and failure, the energy transport of eddies in ocean data used for climate modeling, and lifted flames that lead to clean energy production.
During the talk, I will provide a live demonstration of the effectiveness of some software tools developed at the Center for Extreme Data Management Analysis and Visualization (CEDMAV) and discuss the deployment strategies in an increasing heterogeneous computing environment including DOE parallel supercomputers such as Titan or Mira.
 
 
org: SCOPPOLA Elisabetta